class_docstr: Plot ROC curve
  
  Parameters
  ----------
  fpr : ndarray of shape (>2,), list of lists or list of numbers
      Increasing false positive rates such that element i is the false
      positive rate of predictions with score >= `thresholds[i]`.
  
  tpr : ndarray of shape (>2,), list of lists or list of numbers
      Increasing true positive rates such that element `i` is the true
      positive rate of predictions with score >= `thresholds[i]`.
  
  label : list of str, default: None
      Set curve labels
  
  ax: matplotlib Axes, default: None
      Axes object to draw the plot onto, otherwise uses current Axes
  
  Examples
  --------
  .. plot:: ../examples/roc_binary_classification.py
  
  .. plot:: ../examples/roc_comparison.py
  
  .. plot:: ../examples/roc_multi_classification.py
  
  Notes
  -----
  .. versionadded:: 0.8.4
code: def from_raw_data(cls, y_true, y_score, ax=None):
      fpr, tpr, label = cls._calculate_plotting_data(y_true, y_score)
      return cls(fpr, tpr, label=label, ax=ax).plot()
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn.metrics import roc_curve, auc
  from sklearn.preprocessing import label_binarize
  from ..telemetry import SKLearnEvaluationLogger
  from ..util import is_column_vector, is_row_vector
  from sklearn_evaluation import __version__
  import json
  from pathlib import Path
  from sklearn_evaluation.plot.plot import AbstractPlot, AbstractComposedPlot
  from ploomber_core.exceptions import modify_exceptions
label_desc: Use image node_id 7 for calling the ROC obj's underlying member method code with example usage: obj.from_raw_data(cls, y_true, y_score, ax) and returns: cls